A global analysis of potential connectivity of wildlife populations at the interface of political realms
Background:
As rapid climate change continues, the geographical location of species’ suitable conditions will shift, resulting in a global redistribution of biological diversity. Because of this, development of international policy and, importantly, transboundary diplomacy in response to species’ redistribution has emerged as one of the key issues facing global biodiversity conservation in the 21st century. Although, transboundary diplomacy has been instituted for the management of protected areas and species of conservation concern, there is a critical need for accelerated diplomatic action to address the potential redistribution of thousands of species across geopolitical boundaries. In preparation for this need, knowledge of how permeable each boundary will be redistribution is invaluable. Boundary permeability is related to landscape connectivity, or the extent to which the landscape facilitates or resists movement, and it is influenced by the biogeographic features the boundary overlaps (e.g. rivers, mountains, deserts), the land use (e.g. mining, agriculture) or cover type (e.g. forest), and/or the presence of physical infrastructure (e.g. border walls, transportation routes). Despite the wealth of ad hoc data documenting the connectivity of wildlife populations across geopolitical boundaries, there has yet to be a comprehensive investigation of both climate and habitat connectivity at transboundary interfaces. By attempting such an analysis at a global scale, we may compare, using relative metrics, the permeability of approximately 277 geopolitical borders, ultimately yielding an estimated potential for migration across every geopolitical boundary. The results of this analysis may yield information critical to the facilitation of transboundary diplomacy and conservation policy.
Example of deliverables: Global maps of habitat connectivity
FIGURE 1: Side-by-side animation of the influence of the Human Modification Index (left) on the ecological connectivity (right) of geopolitical boundaries. This figure was made using Circuitscape, with a quantity of randomized paths proportional to the total length of the border.
Research questions:
- Do global geopolitical boundaries represent a substantial hindrance to ecological connectivity?
- Will biologically diverse regions (e.g. tropical rainforest) be disproportionately affected by barriers to connectivity?
- How will climate change impact the accessibility of habitat corridors through geopolitical boundaries?
- Which regions should conservation policies be considered to aid in the connectivity of species?
Proposed methodology:
To model the potential connectivity of these landscape surfaces, we apply principals of circuit theory (McRae 2006), a framework which relates movement of individuals through a landscape to the flow of current through an electrical circuit. In this framework, paths of greatest redundancy (i.e. where many potential paths coincide between origin and destination points) result in high current densities, or corridors, which represent areas where ecological flow would be concentrated. To determine if these low resistance corridors will be accessible to species, we will use climatic similarity as a condition for successful connection between source and target points. This analysis will be conducted using Circuitscape (McRae and Shah 2009) and its derivative for computing omnidirectional connectivity, Omniscape (Landau 2020). These software packages were originally developed in Python, but have been reprogrammed to be implimented in Julia, a very powerful programming language. See toy examples of Circuitscape products (FIGURE 1), as well as visual depiction of how Omniscape works (FIGURE 2).
FIGURE 2: Adapted from McRae et al. (2016) Panel A: ) Left Panel: subset of a resistance layer, with a circular moving window centered on a natural pixel in Forest Park, Portland, OR. Natural and semi-natural lands have low resistance, and human-modified lands have high resistance. Panel B: illustration of the Omniscape ‘moving window’ method, in which a moving window is passed over the resistance and source weight rasters, centering in turn on each pixel. If the center pixel meets the naturalness criteria for being a destination for movement, it is treated as a target for movement. All pixels within the moving window radius that meet the same criteria are considered sources. Current flows from all source pixels to the target pixel, with more current flowing from more natural source pixels. Panel C: resulting current flow pattern when 1 Amp of current is apportioned among all source pixels in proportion to their naturalness and allowed to flow along low resistance routes to the target (yellow representing highest current flow).
Proposed data sources
To conduct these analyses, we will use publically available data (sources listed in Tabel 1) resistance surfaces and/or current sources will be generated using the following publically available data: “Human Modification of Terrestrial Systems,” “Amazon Web Services,” and “Average Temperature.” Biotic data in the form of species richness maps will be used to determine the relationship between connectivity and diversity.
Table 1:
| VARIABLE | GRAIN | EXTENT | SOURCE | LINK |
|---|---|---|---|---|
| Human Modification of Terrestrial Systems | 1 km | global | (Kennedy et al. 2019) | SEDAC |
| Amazon Web Services | 1 km | global (tiles) | (Dwyer et al. 2018) | AWS |
| Average Temperature | 1 km | global | (Fick and Hijmans 2017) | WorldClim |
| Global Terrestrial Species Richness | 10 km | global | (Jenkins, Pimm, and Joppa 2013) | BiodiversityMapping.org |
Example: USA-Mexico border
For example, we’ve performed a connectivity analysis of the border of USA and Mexico using Omniscape. To conduct this analysis, a buffer with radius = 50 km was drawn around the USA-Mexico border. This buffer was used to crop and mask the resistance surface. In this demonstration, a Human Modification Index (Kennedy et al. 2019) was used as the resistance surface. Unlike Circuitscape, when using Omniscape the user specifies source pixels to apply electrical current. Source pixels were designatied as any pixel with a resistance value lower than 80% (i.e., areas of the landscape with greater than 80% resistance are not considered sources of connectivity). To connect source current with a target, a moving window (FIGURE 2) scans the landscape for pixels with sufficient conductance (resistance < 80%). This moving window has a radius of 100 km, and the radius of this window can be considered as the hypothetical maximum dispersal distance required to colonize the target pixel. Omniscape calculates three connectivity metrics (definitions provided by McRae et al. (2016)): Current Flow, Flow Potential, and Normalized Current Flow (the later being synthetic metrics). See Map for the results of this analysis. See Table 1 for definitions of the connectivity metrics derived.
Omniscape: ‘Current Flow’, ‘Potential Flow’ and ‘Normalized Flow’ (see Table 2 for a description of these metrics).
Table 2:
| Connectivity Metric | McRae et al. (2016) Description |
|---|---|
| 1. Current Flow | “indicates where concentrations of natural land and barriers interacted to produce differing patterns of flow. In these raw current flow results, the patterns typically produced by Circuitscape are evident, with current avoiding areas with strong movement barriers, concentrating where flow is channeled through pinch-points, and diffusing in highly intact/highly permeable areas.” |
| 2. Flow Potential | “distinguishes intact (diffuse flow) areas from areas where flow is locally channeled. I.e. given the amount and configuration of natural pixels available to connect within 50 km, how much flow would be expected in the absence of barriers? Areas with higher current flow are located between larger expanses of natural land (i.e., areas that serve as sources or destinations for moving organisms), and thus flow indicates their potential to connect natural lands in the absence of barriers.” |
| 3. Normalized Current Flow | “helps to tease apart the mechanisms behind different flow rates, and better distinguishes broadly natural areas with diffuse flow from areas where barriers are blocking flow or channeling flow through pinch-points. If flow is lower than would be expected without barriers, then barriers are blocking flow from the area. This is evident in urban centers, which have low scores. If flow is higher than would be expected without barriers (i.e., current flow is high relative to regional flow potential), then barriers are channeling flow into the area and potentially creating pinch-points. These areas often show where the best movement options still exist in fragmented landscapes.” |
Literature Cited
Dwyer, J.L., D.P. Roy, B. Sauer, C.B. Jenkerson, H. Zhang, and L Lymburner. 2018. “Analysis Ready Data—Enabling Analysis of the Landsat Archive.” Remote Sensing. https://doi.org/10.3390/rs10091363.
Fick, S.E., and R.J. Hijmans. 2017. “WorldClim 2: New 1km Spatial Resolution Climate Surfaces for Global Land Areas.” International Journal of Climatology 37: 4302–15. https://doi.org/10.1554/05-321.1.
Jenkins, C.N., S.L. Pimm, and L.N. Joppa. 2013. “Global Patterns of Terrestrial Vertebrate Diversity and Conservation.” Proceedings of the National Academy of Sciences 110 (28): E2602–E2610. https://doi.org/10.1073/pnas.1302251110.
Kennedy, C.M., J.R. Oakleaf, D.M. Theobald, S. Baruch-Mordo, and J. Kiesecker. 2019. “Managing the Middle: A Shift in Conservation Priorities Based on the Global Human Modification Gradient.” Global Change Biology 25 (3): 811–26. https://doi.org/10.1111/gcb.14549.
Landau, Vincent A. 2020. “Omniscape.jl: An Efficient and Scalable Implementation of the Omniscape Algorithm in the Julia Programming Language.” https://doi.org/10.5281/zenodo.3955123.
McRae, B.H. 2006. “ISOLATION BY RESISTANCE.” Evolution 60: 1551–61. https://doi.org/10.1554/05-321.1.
McRae, B.H., K. opper, A. Jones, M. Schindel, S. Buttrick, K. Hall, R.S. Unnasch, and J. Platt. 2016. “Conserving Nature’s Stage: Mapping Omnidirectional Connectivity for Resilient Terrestrial Landscapes in the Pacific Northwest.” https://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/oregon/science/Documents/McRae_et_al_2016_PNW_CNS_Connectivity.pdf.
McRae, B.H., and V.B. Shah. 2009. “Circuitscape User’s Guide.” The University of California, Santa Barbara. https://circuitscape.org/docs/.